For people with multiple sclerosis (MS), symptoms vary within and between individuals, consisting of MS-specific and pervasive symptoms. MS-specific symptoms are attributable to brain and spinal cord damage from the disease process, including problems walking, vision loss, numbness, and impaired cognition. Pervasive symptoms?depression, anxiety, fatigue, sleep disturbances, cognitive changes, anger, and pain - are general symptoms that may be triggered by MS or as a result of other known or unknown pathologies. The presence and severity of specific and pervasive symptoms contribute to an overall physical and psychological load leading to adverse outcomes with behavioral, economic, vocational, and societal consequences. Reducing this load is important to prevent adverse outcomes in people with MS, however, little empirical evidence exists to identify those most at risk for poor outcomes using both specific and pervasive symptoms. Prevailing research assessing outcomes in people with MS focuses on problems walking as a primary indicator of disability. Though problems walking is an independent predictor of adverse outcomes in MS, it is a late predictor, and other symptoms often precede walking problems. The impact of early MS-specific symptoms is unknown, leaving a gap in knowledge and disparities in symptom management and prevention of symptom progression for people with MS who do not yet have problems walking. The challenge now is to identify those most at risk for adverse outcomes early in the MS disease process and to intervene by improving self-management capacity before symptoms worsen and negatively impact day-to-day functions. Understanding the relationship between early MS-specific symptoms, longitudinal pervasive symptoms, and individual characteristics (age, biological sex, employment status, etc.) may assist in identifying those at increased risk for adverse outcomes, allowing for the development of personalized preventative interventions. The purpose of this study is to characterize?or phenotype?symptom experiences using the MURDOCK-MS dataset of 958 adults with MS. This is the first study to analyze symptom data from the MURDOCK-MS dataset. I will create symptom phenotypes using both MS-specific symptoms from the first MS-attack and five years of pervasive symptom data. Guided by the NIH Symptom Science Model and the Adaptive Leadership Framework, this study will: Aim 1) Develop MS-specific symptom clusters for adults with MS using symptoms from the first MS-attack; Aim 2) Classify adults with MS into trajectory typologies based on pervasive symptom severity over five years; and Aim 3) Create MS symptom phenotypes that describe the relationship between MS-specific symptom clusters (from Aim 1) to (a) pervasive symptom trajectory typologies (from Aim 2), and (b) Individual characteristics (demographic variables and covariates). Resulting symptom phenotypes will describe MS symptom experiences and inform my future research regarding symptom self-management and prevention of adverse symptoms in adults with MS.